{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import random\n",
    "import cPickle as pickle\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_colwidth\",100)\n",
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "\n",
    "np.random.seed(1337)\n",
    "import keras\n",
    "\n",
    "from keras.preprocessing import sequence\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense\n",
    "from keras.layers.core import Dropout\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.core import Flatten\n",
    "from keras.layers.convolutional import Convolution1D, MaxPooling1D\n",
    "from keras.constraints import maxnorm\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "### Plotting settings ###\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.labelpad'] = 5\n",
    "plt.rcParams['axes.linewidth']= 2.5\n",
    "plt.rcParams['xtick.labelsize']= 24\n",
    "plt.rcParams['ytick.labelsize']= 24\n",
    "plt.rcParams['axes.labelsize'] = 20\n",
    "plt.rcParams['figure.dpi'] = 200\n",
    "plt.rcParams['lines.linewidth'] = 7\n",
    "plt.rcParams['legend.markerscale'] = 1.5\n",
    "plt.rcParams['legend.fontsize'] = 20\n",
    "plt.rcParams['xtick.major.size'] = 12\n",
    "plt.rcParams['ytick.major.size'] = 12\n",
    "plt.rcParams['xtick.minor.size'] = 8\n",
    "plt.rcParams['ytick.minor.size'] = 8\n",
    "plt.rcParams['xtick.minor.width'] = 3\n",
    "plt.rcParams['ytick.minor.width'] = 3\n",
    "plt.rcParams['xtick.major.width'] = 4\n",
    "plt.rcParams['ytick.major.width'] = 4\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n",
    "plt.rcParams['figure.autolayout'] = True\n",
    "plt.rcParams['mathtext.default'] = 'regular'\n",
    "plt.rcParams['xtick.color'] = 'black'\n",
    "plt.rcParams['ytick.color'] = 'black'\n",
    "plt.rcParams['axes.labelcolor'] = \"black\"\n",
    "plt.rcParams['axes.edgecolor'] = 'black'\n",
    "\n",
    "def train_model(x, y, border_mode='same', inp_len=50, nodes=40, layers=3, filter_len=8, nbr_filters=120,\n",
    "                dropout1=0, dropout2=0, dropout3=0, nb_epoch=3):\n",
    "    ''' Build model archicture and fit.'''\n",
    "    model = Sequential()\n",
    "    if layers >= 1:\n",
    "        model.add(Convolution1D(nb_filter=nbr_filters,filter_length=filter_len,input_dim=4,input_length=inp_len,border_mode=border_mode, activation='relu'))\n",
    "    if layers >= 2:\n",
    "        model.add(Convolution1D(nb_filter=nbr_filters,filter_length=filter_len,input_dim=4,input_length=inp_len,border_mode=border_mode, activation='relu'))\n",
    "        model.add(Dropout(dropout1))\n",
    "    if layers >= 3:\n",
    "        model.add(Convolution1D(nb_filter=nbr_filters,filter_length=filter_len,input_dim=4,input_length=inp_len,border_mode=border_mode, activation='relu'))\n",
    "        model.add(Dropout(dropout2))\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(nodes))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(dropout3))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    #compile the model\n",
    "    adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
    "    model.compile(loss='mean_squared_error', optimizer=adam)\n",
    "\n",
    "    model.fit(x, y, batch_size=128, nb_epoch=nb_epoch, verbose=1)\n",
    "    return model\n",
    "\n",
    "def test_data(df, model, test_seq, obs_col, output_col='pred'):\n",
    "    scaler = preprocessing.StandardScaler()\n",
    "    scaler.fit(df[obs_col].reshape(-1,1))\n",
    "    #df.loc[:,'obs_stab'] = test_df['stab_df']\n",
    "    predictions = model.predict(test_seq).reshape(-1)\n",
    "    df.loc[:,output_col] = scaler.inverse_transform(predictions)\n",
    "    return df\n",
    "\n",
    "def one_hot_encode(df, col='utr', seq_len=50):\n",
    "    # Dictionary returning one-hot encoding of nucleotides. \n",
    "    nuc_d = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n",
    "    \n",
    "    # Creat empty matrix.\n",
    "    vectors=np.empty([len(df),seq_len,4])\n",
    "    \n",
    "    # Iterate through UTRs and one-hot encode\n",
    "    for i,seq in enumerate(df[col].str[:seq_len]): \n",
    "        seq = seq.lower()\n",
    "        a = np.array([nuc_d[x] for x in seq])\n",
    "        vectors[i] = a\n",
    "    return vectors\n",
    "\n",
    "def r2(x,y):\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(data[x],data[y])\n",
    "    return r_value**2\n",
    "\n",
    "def eval_data(df, model, test_seq, obs_col, output_col='pred'):\n",
    "    scaler = preprocessing.StandardScaler()\n",
    "    scaler.fit(df[obs_col].reshape(-1,1))\n",
    "    #df.loc[:,'obs_stab'] = test_df['stab_df']\n",
    "    predictions = model.predict(test_seq).reshape(-1)\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(df[obs_col],scaler.inverse_transform(predictions))\n",
    "    return r_value**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load all EGFP data. Rename total reads and RL columns to represent each data set in the merged dataframe.\n",
    "e1 & e2 = unmodified RNA EGFP........c1 & c2 = unmodified RNA mCherry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "323231\n"
     ]
    }
   ],
   "source": [
    "e1 = pd.read_pickle('../../data/egfp_unmod_1.pkl')\n",
    "e1['e1_rl'] = e1['rl']\n",
    "e1['e1_total'] = e1['total_reads']\n",
    "e1['utr'] = e1['utr'].str[:50]\n",
    "\n",
    "e2 = pd.read_pickle('../../data/egfp_unmod_2.pkl')\n",
    "e2['e2_rl'] = e2['rl']\n",
    "e2['e2_total'] = e2['total']\n",
    "e2['utr'] = e2['utr'].str[:50]\n",
    "\n",
    "\n",
    "df = pd.merge(e1, e2, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total']]\n",
    "\n",
    "df.dropna(how='any', inplace=True)\n",
    "print len(df)\n",
    "\n",
    "e1 = None\n",
    "e2 = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.sort_values('e2_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:300000]\n",
    "df.sort_values('e1_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:280000]\n",
    "\n",
    "# Train / test split --- test set is the 20k sequences with the highest number of reads\n",
    "e_test = df[:20000]\n",
    "e_test.reset_index(inplace=True, drop=True)\n",
    "e_train = df[20000:]\n",
    "e_train.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# One-hot encode UTR sequences\n",
    "seq_e_train = one_hot_encode(e_train,seq_len=50)\n",
    "seq_e_test = one_hot_encode(e_test, seq_len=50)\n",
    "\n",
    "# Scale ribosome loads\n",
    "e_train.loc[:,'e1_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'e1_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'e2_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'e2_rl'].reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and process mCherry data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191797\n",
      "20000 150000\n"
     ]
    }
   ],
   "source": [
    "c1 = pd.read_pickle('../../data/mcherry_1.pkl')\n",
    "c2 = pd.read_pickle('../../data/mcherry_2.pkl')\n",
    "c1 = c1.rename(columns={'rl':'c1_rl','total':'c1_total'})\n",
    "c2 = c2.rename(columns={'rl':'c2_rl','total':'c2_total'})\n",
    "mc = pd.merge(c1,c2, on='utr', how='inner')\n",
    "print len(mc)\n",
    "\n",
    "\n",
    "mc.sort_values('c1_total', inplace=True, ascending=False)\n",
    "mc.reset_index(inplace=True, drop=True)\n",
    "mc = mc[:180000]\n",
    "mc.sort_values('c2_total', inplace=True, ascending=False)\n",
    "mc.reset_index(inplace=True, drop=True)\n",
    "mc = mc[:170000]\n",
    "\n",
    "mc.sort_values('c1_total', inplace=True, ascending=False)\n",
    "mc.reset_index(inplace=True, drop=True)\n",
    "\n",
    "c_train = mc[20000:]\n",
    "#c_train.reset_index(inplace=True, drop=True)\n",
    "\n",
    "c_test = mc[:20000]\n",
    "#c_test.reset_index(inplace=True, drop=True)\n",
    "print len(c_test), len(c_train)\n",
    "\n",
    "# One-hot encode UTR sequences\n",
    "seq_c_train = one_hot_encode(c_train,seq_len=50)\n",
    "seq_c_test = one_hot_encode(c_test, seq_len=50)\n",
    "\n",
    "c_train.loc[:,'c1_scaled_rl'] = preprocessing.StandardScaler().fit_transform(c_train.loc[:,'c1_rl'].reshape(-1,1))\n",
    "c_train.loc[:,'c2_scaled_rl'] = preprocessing.StandardScaler().fit_transform(c_train.loc[:,'c2_rl'].reshape(-1,1))\n",
    "c_train = c_train[['utr', 'c1_scaled_rl', 'c2_scaled_rl', 'c1_total', 'c2_total', 'c1_rl', 'c2_rl']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train EGFP models and test on all data (EGFP and mCherry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "260000/260000 [==============================] - 108s - loss: 0.3002   \n",
      "Epoch 2/3\n",
      "260000/260000 [==============================] - 107s - loss: 0.2040   \n",
      "Epoch 3/3\n",
      "260000/260000 [==============================] - 107s - loss: 0.1852   \n",
      "e1_model (0.90662597436881021, 0.86244152436949972, 0.75693153645078082, 0.73745088582440455)\n",
      "\n",
      "Epoch 1/3\n",
      "260000/260000 [==============================] - 108s - loss: 0.3648   \n",
      "Epoch 2/3\n",
      "260000/260000 [==============================] - 107s - loss: 0.2637   \n",
      "Epoch 3/3\n",
      "260000/260000 [==============================] - 107s - loss: 0.2394   \n",
      "e2_model (0.87934123432224609, 0.88562139481990909, 0.69521422948032707, 0.67926610422743772)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dictionaries to store models and model performance on all data\n",
    "model_dict = {}\n",
    "performance_dict = {}\n",
    "\n",
    "# Train EGFP data & test on all samples\n",
    "for sample in ['e1', 'e2']:\n",
    "    m_name = sample + '_model'\n",
    "    \n",
    "    # Train\n",
    "    model_dict[m_name] = train_model(seq_e_train, e_train[sample + '_scaled_rl'], nb_epoch=3,border_mode='same',\n",
    "                       inp_len=50, nodes=40, layers=3, nbr_filters=120, filter_len=8, dropout1=0,\n",
    "                       dropout2=0,dropout3=0.2)\n",
    "    \n",
    "    # Save model\n",
    "    model = model_dict[m_name]\n",
    "    \n",
    "    # Evaluate on self and the other data sets\n",
    "    e1 = eval_data(e_test, model, seq_e_test, 'e1_rl')\n",
    "    e2 = eval_data(e_test, model, seq_e_test, 'e2_rl')\n",
    "    c1 = eval_data(c_test, model, seq_c_test, 'c1_rl')\n",
    "    c2 = eval_data(c_test, model, seq_c_test, 'c2_rl')\n",
    "    \n",
    "    # Save model performance\n",
    "    performance_dict[m_name] = (e1, e2, c1, c2)\n",
    "    print m_name, performance_dict[m_name]\n",
    "    print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('./saved_data/cds_e_model_performance.dict', 'wb') as f:\n",
    "    pickle.dump(performance_dict, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train mCherry models and test on all data (EGFP and mCherry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "150000/150000 [==============================] - 37s - loss: 0.3478    \n",
      "Epoch 2/3\n",
      "150000/150000 [==============================] - 37s - loss: 0.2381    \n",
      "Epoch 3/3\n",
      "150000/150000 [==============================] - 37s - loss: 0.2105    \n",
      "c1_model (0.87583118753653766, 0.81697121571373477, 0.82973391226945992, 0.80526756504980812)\n",
      "\n",
      "Epoch 1/3\n",
      "150000/150000 [==============================] - 37s - loss: 0.3661    \n",
      "Epoch 2/3\n",
      "150000/150000 [==============================] - 37s - loss: 0.2365    \n",
      "Epoch 3/3\n",
      "150000/150000 [==============================] - 37s - loss: 0.2126    \n",
      "c2_model (0.84876753674603, 0.79504030270753046, 0.81445391310172155, 0.82348411063545446)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dictionaries to store models and model performance on all data\n",
    "model_dict = {}\n",
    "performance_dict = {}\n",
    "\n",
    "# Train egfp data & test on all samples\n",
    "for sample in ['c1', 'c2']:\n",
    "    m_name = sample + '_model'\n",
    "    \n",
    "    # Train\n",
    "    model_dict[m_name] = train_model(seq_c_train, c_train[sample + '_scaled_rl'], nb_epoch=3,border_mode='same',\n",
    "                       inp_len=50, nodes=40, layers=2, nbr_filters=120, filter_len=8, dropout1=0,\n",
    "                       dropout2=0.2,dropout3=0)\n",
    "    \n",
    "    # Save model\n",
    "    model = model_dict[m_name]\n",
    "    \n",
    "    # Evaluate on self and the other data sets\n",
    "    e1 = eval_data(e_test, model, seq_e_test, 'e1_rl')\n",
    "    e2 = eval_data(e_test, model, seq_e_test, 'e2_rl')\n",
    "    c1 = eval_data(c_test, model, seq_c_test, 'c1_rl')\n",
    "    c2 = eval_data(c_test, model, seq_c_test, 'c2_rl')\n",
    "    \n",
    "    # Save model performance\n",
    "    performance_dict[m_name] = (e1, e2, c1, c2)\n",
    "    print m_name, performance_dict[m_name]\n",
    "    print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('./saved_data/cds_c_model_performance.dict', 'wb') as f:\n",
    "    pickle.dump(performance_dict, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Load performance dictionaries, combine into a single dictionary, and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_perf = np.zeros((4,4))\n",
    "new_dict = {}\n",
    "\n",
    "# Load EGFP model performance\n",
    "d = pickle.load(open('./saved_data/cds_e_model_performance.dict', 'rb'))\n",
    "new_dict.update(d)\n",
    "\n",
    "# Load mCherry model performance\n",
    "d = pickle.load(open('./saved_data/cds_c_model_performance.dict', 'rb'))\n",
    "new_dict.update(d)\n",
    "\n",
    "a = np.array((new_dict['e1_model'], new_dict['e2_model'], new_dict['c1_model'], new_dict['c2_model']))\n",
    "a = a.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I+vuCw+JSQIWFheGf//wn3n77bbuNQ/53Xl4eZs6ciddff906z8qVK3Ho0CG7jl7+tyRJ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/DmNv0seCcjjR5vUKkP4yZWNVqc8n0N68LaFosDCwz2Lw7SLahY1WQxc1uYmu35auzYL2pNTv++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\niIiIiCKGgYOIiIiIiCLm/wNl9PYAtxw+FQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ee938ccd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax =sns.heatmap(a, annot=True, cmap='YlOrRd', vmin=0.5, vmax=1, annot_kws={'size':'25', 'color':'k'}, cbar=False, square=True)\n",
    "f = ax.get_figure()\n",
    "f.set_size_inches((8,8))\n",
    "ax.set_xticklabels(['EGFP${_1}$', 'EGFP${_2}$', r'mC${_1}$', r'mC${_2}$'], size=27, fontdict={'family':'arial'})\n",
    "ax.set_yticklabels([r'mC${_2}$', r'mC${_1}$', 'EGFP${_2}$', 'EGFP${_1}$'], size=27, fontdict={'family':'arial'})\n",
    "ax.set_xlabel('Model Training Data', size = 40, fontdict={'family':'arial'})\n",
    "ax.set_ylabel('Model Test Data', size=40, fontdict={'family':'arial'});\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('top');\n",
    "ax.xaxis.set_label_position('top');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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